Ren Wei1, Chendan Jiang1, Jun Gao1, Ping Xu1, Debing Zhang2, Zhicheng Sun2, Xiaohai Liu1, Kan Deng1, Xinjie Bao1, Guoqiang Sun3, Yong Yao1, Lin Lu4, Huijuan Zhu4, Renzhi Wang1, Ming Feng5. 1. Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. 2. Deepglint Co., Ltd., Beijing, China. 3. Information office, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. 4. Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. 5. Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, FengMing@pumch.cn.
Abstract
BACKGROUND: Deep learning has the potential to assist the medical diagnostic process. We aimed to identify facial anomalies associated with endocrinal disorders using a deep-learning approach to facilitate the process of diagnosis and follow-up. METHODS: We collected facial images of patients with hypercortisolism and acromegaly, and we augmented these images with additional negative samples from public databases. A model with a pretrained deep-learning network was constructed to automatically identify these hypersecretion statuses based on characteristic facial changes. We compared its performance to that of endocrine experts and further investigated key factors upon which the best performing model focused. FINDINGS: The model achieved areas under the receiver operating characteristic curve of 0.9647 (Cushing's syndrome) and 0.9556 (acromegaly), accuracies of 0.9593 (Cushing's syndrome) and 0.9479 (acromegaly), and recalls of 0.7593 (Cushing's syndrome) and 0.8089 (acromegaly). It performed better than any level of our endocrine experts. Furthermore, the regions of interest on the part of the machine were primarily the same as those upon which the humans focused. INTERPRETATION: Our findings suggest that the deep-learning model learned the facial characters based merely on labeled data without learning prerequisite medical knowledge, and its performance was comparable with professional medical practitioners. The model has the potential to assist in the diagnosis and follow-up of these hypersecretion statuses.
BACKGROUND: Deep learning has the potential to assist the medical diagnostic process. We aimed to identify facial anomalies associated with endocrinal disorders using a deep-learning approach to facilitate the process of diagnosis and follow-up. METHODS: We collected facial images of patients with hypercortisolism and acromegaly, and we augmented these images with additional negative samples from public databases. A model with a pretrained deep-learning network was constructed to automatically identify these hypersecretion statuses based on characteristic facial changes. We compared its performance to that of endocrine experts and further investigated key factors upon which the best performing model focused. FINDINGS: The model achieved areas under the receiver operating characteristic curve of 0.9647 (Cushing's syndrome) and 0.9556 (acromegaly), accuracies of 0.9593 (Cushing's syndrome) and 0.9479 (acromegaly), and recalls of 0.7593 (Cushing's syndrome) and 0.8089 (acromegaly). It performed better than any level of our endocrine experts. Furthermore, the regions of interest on the part of the machine were primarily the same as those upon which the humans focused. INTERPRETATION: Our findings suggest that the deep-learning model learned the facial characters based merely on labeled data without learning prerequisite medical knowledge, and its performance was comparable with professional medical practitioners. The model has the potential to assist in the diagnosis and follow-up of these hypersecretion statuses.